






'''simple docstring'''







import os
import string
import sys


__lowerCAmelCase        =							1 << 8

__lowerCAmelCase        =							{
    "tab": ord("\t"),
    "newline": ord("\r"),
    "esc": 27,
    "up": 65 + ARROW_KEY_FLAG,
    "down": 66 + ARROW_KEY_FLAG,
    "right": 67 + ARROW_KEY_FLAG,
    "left": 68 + ARROW_KEY_FLAG,
    "mod_int": 91,
    "undefined": sys.maxsize,
    "interrupt": 3,
    "insert": 50,
    "delete": 51,
    "pg_up": 53,
    "pg_down": 54,
}

__lowerCAmelCase        =							KEYMAP["up"]
__lowerCAmelCase        =							KEYMAP["left"]

if sys.platform == "win32":
  __lowerCAmelCase        =							[]
  __lowerCAmelCase        =							{
      b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
      b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
      b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
      b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
      b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
      b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
      b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
      b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
  }

for i in range(10):
  __lowerCAmelCase        =							ord(str(i))


def       __UpperCamelCase					(      ):
       """simple docstring"""




       if os.name == "nt":
              import msvcrt

              a_						     =			'mbcs'
              # Flush the keyboard buffer
              while msvcrt.kbhit():
                     msvcrt.getch()
              if len(lowercase_   ) == 0:
                     # Read the keystroke
                     a_						     =			msvcrt.getch()

                     # If it is a prefix char, get second part
                     if ch in (b"\x00", b"\xe0"):
                            a_						     =			ch + msvcrt.getch()
                            # Translate actual Win chars to bullet char types
                            try:
                                   a_						     =			chr(WIN_KEYMAP[cha]   )
                                   WIN_CH_BUFFER.append(chr(KEYMAP['mod_int']   )   )
                                   WIN_CH_BUFFER.append(lowercase_   )
                                   if ord(lowercase_   ) in (
                                       KEYMAP["insert"] - 1 << 9,
                                       KEYMAP["delete"] - 1 << 9,
                                       KEYMAP["pg_up"] - 1 << 9,
                                       KEYMAP["pg_down"] - 1 << 9,
                                   ):
                                          WIN_CH_BUFFER.append(chr(126   )   )
                                   a_						     =			chr(KEYMAP['esc']   )
                            except KeyError:
                                   a_						     =			cha[1]
                     else:
                            a_						     =			ch.decode(lowercase_   )
              else:
                     a_						     =			WIN_CH_BUFFER.pop(0   )
       elif os.name == "posix":
              import termios
              import tty

              a_						     =			sys.stdin.fileno()
              a_						     =			termios.tcgetattr(lowercase_   )
              try:
                     tty.setraw(lowercase_   )
                     a_						     =			sys.stdin.read(1   )
              finally:
                     termios.tcsetattr(lowercase_   ,       termios.TCSADRAIN   ,       lowercase_   )
       return ch


def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			get_raw_chars()
       if ord(lowercase_   ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
              return char

       elif ord(lowercase_   ) == KEYMAP["esc"]:
              a_						     =			get_raw_chars()
              if ord(lowercase_   ) == KEYMAP["mod_int"]:
                     a_						     =			get_raw_chars()
                     if ord(lowercase_   ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowercase_   ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
                            return chr(ord(lowercase_   ) + ARROW_KEY_FLAG   )
                     else:
                            return KEYMAP["undefined"]
              else:
                     return get_raw_chars()

       else:
              if char in string.printable:
                     return char
              else:
                     return KEYMAP["undefined"]












'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase        =							False




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 32







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			VQModel(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
         return tokenizer







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         return CLIPTextModel(UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)

         a_						     =			12
         a_						     =			12

         a_						     =			{
             'attention_bias': True,
             'cross_attention_dim': 32,
             'attention_head_dim': height * width,
             'num_attention_heads': 1,
             'num_vector_embeds': self.num_embed,
             'num_embeds_ada_norm': self.num_embeds_ada_norm,
             'norm_num_groups': 32,
             'sample_size': width,
             'activation_fn': 'geglu-approximate',
         }

         a_						     =			TransformeraDModel(**UpperCamelCase__	)
         return model







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase__	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(
             learnable=UpperCamelCase__   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'	)

         a_						     =			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'	)
         a_						     =			pipeline.to(UpperCamelCase__	)
         pipeline.set_progress_bar_config(disable=UpperCamelCase__	)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipeline(
             'teddy bear playing in the pool'   ,	num_images_per_prompt=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)

         a_						     =			output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image	).max() < 2.0




